| 1. |
- Wang, Fang, et al.
(författare)
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Numerical Reconstruction of Cyclist Impact Accidents : Can Helmets Protect the Head-Neck of Cyclists?
- 2023
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Ingår i: Biomimetics. - : MDPI AG. - 2313-7673. ; 8:6
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Tidskriftsartikel (refereegranskat)abstract
- Cyclists are vulnerable road users and often suffer head-neck injuries in car–cyclist accidents. Wearing a helmet is currently the most prevalent protection method against such injuries. Today, there is an ongoing debate about the ability of helmets to protect the cyclists’ head-neck from injury. In the current study, we numerically reconstructed five real-world car–cyclist impact accidents, incorporating previously developed finite element models of four cyclist helmets to evaluate their protective performances. We made comparative head-neck injury predictions for unhelmeted and helmeted cyclists. The results show that helmets could clearly lower the risk of severe (AIS 4+) brain injury and skull fracture, as assessed by the predicted head injury criterion (HIC), while a relatively limited decrease in AIS 4+ brain injury risk can be achieved in terms of the analysis of CSDM0.25. Assessment using the maximum principal strain (MPS0.98) and head impact power (HIP) criteria suggests that helmets could lower the risk of diffuse axonal injury and subdural hematoma of the cyclist. The helmet efficacy in neck protection depends on the impact scenario. Therefore, wearing a helmet does not seem to cause a significant neck injury risk level increase to the cyclist. Our work presents important insights into the helmet’s efficacy in protecting the head-neck of cyclists and motivates further optimization of protective equipment.
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| 2. |
- Liang, Yajun, et al.
(författare)
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Metabolic syndrome in patients with first-ever ischemic stroke : prevalence and association with coronary heart disease
- 2022
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- The metabolic syndrome (MetS) has been well linked with coronary heart disease (CHD) in the general population, but studies have rarely explored their association among patients with stroke. We examine prevalence of MetS and its association with CHD in patients with first-ever ischemic stroke. This hospital-based study included 1851 patients with first-ever ischemic stroke (mean age 61.2 years, 36.5% women) who were hospitalized into two university hospitals in Shandong, China (January 2016–February 2017). Data were collected through interviews, physical examinations, and laboratory tests. MetS was defined following the National Cholesterol Education Program (NCEP) criteria, the International Diabetes Federation (IDF) criteria, and the Chinese Diabetes Society (CDS) criteria. CHD was defined following clinical criteria. Data were analyzed using binary logistic regression models. The overall prevalence of MetS was 33.4% by NECP criteria, 47.2% by IDF criteria, and 32.5% by CDS criteria, with the prevalence being decreased with age and higher in women than in men (p < 0.05). High blood pressure, high triglycerides, and low HDL-C were significantly associated with CHD (multi-adjusted odds ratio [OR] range 1.27–1.38, p < 0.05). The multi-adjusted OR of CHD associated with MetS defined by the NECP criteria, IDF criteria, and CDS criteria (vs. no MetS) was 1.27 (95% confidence interval 1.03–1.57), 1.44 (1.18–1.76), and 1.27 (1.03–1.57), respectively. In addition, having 1–2 abnormal components (vs. none) of MetS was associated with CHD (multi-adjusted OR range 1.66–1.72, p < 0.05). MetS affects over one-third of patients with first-ever ischemic stroke. MetS is associated with an increased likelihood of CHD in stroke patients.
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| 3. |
- Zhong, Wanjun, et al.
(författare)
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MemoryBank: Enhancing Large Language Models with Long-Term Memory
- 2024
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Ingår i: <em>Proceedings Of The Aaai Conference On Artificial Intelligence</em>. - : Association for the Advancement of Artificial Intelligence (AAAI). ; , s. 19724-19731
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Konferensbidrag (refereegranskat)abstract
- Large Language Models (LLMs) have drastically reshaped our interactions with artificial intelligence (AI) systems, showcasing impressive performance across an extensive array of tasks. Despite this, a notable hindrance remains—the deficiency of a long-term memory mechanism within these models. This shortfall becomes increasingly evident in situations demanding sustained interaction, such as personal companion systems, psychological counseling, and secretarial assistance. Recognizing the necessity for long-term memory, we propose MemoryBank, a novel memory mechanism tailored for LLMs. MemoryBank enables the models to summon relevant memories, continually evolve through continuous memory updates, comprehend, and adapt to a user’s personality over time by synthesizing information from previous interactions. To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory. This mechanism permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a more human-like memory mechanism and enriched user experience. MemoryBank is versatile in accommodating both closed-source models like ChatGPT and open-source models such as ChatGLM. To validate MemoryBank’s effectiveness, we exemplify its application through the creation of an LLM-based chatbot named SiliconFriend in a long-term AI Companion scenario. Further tuned with psychological dialog data, SiliconFriend displays heightened empathy and discernment in its interactions. Experiment involves both qualitative analysis with real-world user dialogs and quantitative analysis with simulated dialogs. In the latter, ChatGPT acts as multiple users with diverse characteristics and generates long-term dialog contexts covering a wide array of topics. The results of our analysis reveal that SiliconFriend, equipped with MemoryBank, exhibits a strong capability for long-term companionship as it can provide emphatic response, recall relevant memories and understand user personality.
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